Lecture

Singular Value Decomposition

In course

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Description

This lecture covers the concept of Singular Value Decomposition (SVD) and its application in unsupervised learning, focusing on dimensionality reduction and the properties of SVD. It explains how SVD can be used to decompose a matrix into singular vectors and values, and how it relates to eigen decomposition. The lecture also discusses the importance of SVD in data analysis and its connection to real symmetric matrices.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_u317uef3

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Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Information engineering

Data science: Topics in data science, Dimensionality reduction

Biology

Genetics: Genomics

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